Overview

Dataset statistics

Number of variables19
Number of observations41714
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 MiB
Average record size in memory131.0 B

Variable types

Categorical6
Numeric10
Boolean3

Alerts

Shared Room is highly imbalanced (93.6%)Imbalance
Price is highly skewed (γ1 = 23.81247397)Skewed
City Center (km) has unique valuesUnique
Metro Distance (km) has unique valuesUnique
Attraction Index has unique valuesUnique
Restraunt Index has unique valuesUnique
Bedrooms has 3745 (9.0%) zerosZeros

Reproduction

Analysis started2024-05-15 12:45:55.431548
Analysis finished2024-05-15 12:46:06.859889
Duration11.43 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

City
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
Rome
9027 
Paris
6688 
Lisbon
5763 
Athens
5280 
Budapest
4022 
Other values (4)
10934 

Length

Max length9
Median length8
Mean length5.9530373
Min length4

Characters and Unicode

Total characters248325
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmsterdam
2nd rowAmsterdam
3rd rowAmsterdam
4th rowAmsterdam
5th rowAmsterdam

Common Values

ValueCountFrequency (%)
Rome 9027
21.6%
Paris 6688
16.0%
Lisbon 5763
13.8%
Athens 5280
12.7%
Budapest 4022
9.6%
Vienna 3537
 
8.5%
Barcelona 2833
 
6.8%
Berlin 2484
 
6.0%
Amsterdam 2080
 
5.0%

Length

2024-05-15T14:46:06.922904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:07.064936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rome 9027
21.6%
paris 6688
16.0%
lisbon 5763
13.8%
athens 5280
12.7%
budapest 4022
9.6%
vienna 3537
 
8.5%
barcelona 2833
 
6.8%
berlin 2484
 
6.0%
amsterdam 2080
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e 29263
11.8%
s 23833
 
9.6%
n 23434
 
9.4%
a 21993
 
8.9%
i 18472
 
7.4%
o 17623
 
7.1%
r 14085
 
5.7%
m 13187
 
5.3%
t 11382
 
4.6%
B 9339
 
3.8%
Other values (12) 65714
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29263
11.8%
s 23833
 
9.6%
n 23434
 
9.4%
a 21993
 
8.9%
i 18472
 
7.4%
o 17623
 
7.1%
r 14085
 
5.7%
m 13187
 
5.3%
t 11382
 
4.6%
B 9339
 
3.8%
Other values (12) 65714
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29263
11.8%
s 23833
 
9.6%
n 23434
 
9.4%
a 21993
 
8.9%
i 18472
 
7.4%
o 17623
 
7.1%
r 14085
 
5.7%
m 13187
 
5.3%
t 11382
 
4.6%
B 9339
 
3.8%
Other values (12) 65714
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29263
11.8%
s 23833
 
9.6%
n 23434
 
9.4%
a 21993
 
8.9%
i 18472
 
7.4%
o 17623
 
7.1%
r 14085
 
5.7%
m 13187
 
5.3%
t 11382
 
4.6%
B 9339
 
3.8%
Other values (12) 65714
26.5%

Price
Real number (ℝ)

SKEWED 

Distinct8087
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.09442
Minimum34.779339
Maximum18545.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:07.204968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum34.779339
5-th percentile92.798725
Q1144.01608
median203.81927
Q3297.37336
95-th percentile602.02001
Maximum18545.45
Range18510.671
Interquartile range (IQR)153.35727

Descriptive statistics

Standard deviation279.40849
Coefficient of variation (CV)1.0742579
Kurtosis1149.8836
Mean260.09442
Median Absolute Deviation (MAD)70.34181
Skewness23.812474
Sum10849579
Variance78069.106
MonotonicityNot monotonic
2024-05-15T14:46:07.316993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184.4621607 188
 
0.5%
161.5505108 167
 
0.4%
126.7154513 162
 
0.4%
115.9984065 145
 
0.3%
138.9637476 133
 
0.3%
207.6076029 128
 
0.3%
104.2813957 128
 
0.3%
149.8608936 127
 
0.3%
103.8038015 126
 
0.3%
230.5192528 116
 
0.3%
Other values (8077) 40294
96.6%
ValueCountFrequency (%)
34.77933919 1
 
< 0.1%
37.12929454 1
 
< 0.1%
39.00925882 1
 
< 0.1%
40.1842365 1
 
< 0.1%
42.88425937 5
< 0.1%
44.1791606 1
 
< 0.1%
45.22766152 8
< 0.1%
46.05709209 3
 
< 0.1%
46.16502238 2
 
< 0.1%
46.39936259 3
 
< 0.1%
ValueCountFrequency (%)
18545.45028 1
< 0.1%
16445.61469 1
< 0.1%
13664.30592 1
< 0.1%
13656.35883 1
< 0.1%
12942.99138 1
< 0.1%
8130.668104 1
< 0.1%
7782.907225 1
< 0.1%
6943.70098 2
< 0.1%
6942.770033 2
< 0.1%
6086.298787 1
< 0.1%

Day
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
Weekday
20886 
Weekend
20828 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters291998
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekday
2nd rowWeekday
3rd rowWeekday
4th rowWeekday
5th rowWeekday

Common Values

ValueCountFrequency (%)
Weekday 20886
50.1%
Weekend 20828
49.9%

Length

2024-05-15T14:46:07.418015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:07.511036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
weekday 20886
50.1%
weekend 20828
49.9%

Most occurring characters

ValueCountFrequency (%)
e 104256
35.7%
W 41714
14.3%
k 41714
14.3%
d 41714
14.3%
a 20886
 
7.2%
y 20886
 
7.2%
n 20828
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 104256
35.7%
W 41714
14.3%
k 41714
14.3%
d 41714
14.3%
a 20886
 
7.2%
y 20886
 
7.2%
n 20828
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 104256
35.7%
W 41714
14.3%
k 41714
14.3%
d 41714
14.3%
a 20886
 
7.2%
y 20886
 
7.2%
n 20828
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 104256
35.7%
W 41714
14.3%
k 41714
14.3%
d 41714
14.3%
a 20886
 
7.2%
y 20886
 
7.2%
n 20828
 
7.1%

Room Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
Entire home/apt
28264 
Private room
13134 
Shared room
 
316

Length

Max length15
Median length15
Mean length14.025123
Min length11

Characters and Unicode

Total characters585044
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowPrivate room
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 28264
67.8%
Private room 13134
31.5%
Shared room 316
 
0.8%

Length

2024-05-15T14:46:07.601057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:07.711082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 28264
33.9%
home/apt 28264
33.9%
room 13450
16.1%
private 13134
15.7%
shared 316
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 69978
12.0%
t 69662
11.9%
o 55164
9.4%
r 55164
9.4%
a 41714
 
7.1%
41714
 
7.1%
m 41714
 
7.1%
i 41398
 
7.1%
h 28580
 
4.9%
p 28264
 
4.8%
Other values (7) 111692
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 585044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 69978
12.0%
t 69662
11.9%
o 55164
9.4%
r 55164
9.4%
a 41714
 
7.1%
41714
 
7.1%
m 41714
 
7.1%
i 41398
 
7.1%
h 28580
 
4.9%
p 28264
 
4.8%
Other values (7) 111692
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 585044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 69978
12.0%
t 69662
11.9%
o 55164
9.4%
r 55164
9.4%
a 41714
 
7.1%
41714
 
7.1%
m 41714
 
7.1%
i 41398
 
7.1%
h 28580
 
4.9%
p 28264
 
4.8%
Other values (7) 111692
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 585044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 69978
12.0%
t 69662
11.9%
o 55164
9.4%
r 55164
9.4%
a 41714
 
7.1%
41714
 
7.1%
m 41714
 
7.1%
i 41398
 
7.1%
h 28580
 
4.9%
p 28264
 
4.8%
Other values (7) 111692
19.1%

Shared Room
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
False
41398 
True
 
316
ValueCountFrequency (%)
False 41398
99.2%
True 316
 
0.8%
2024-05-15T14:46:07.808104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
False
28580 
True
13134 
ValueCountFrequency (%)
False 28580
68.5%
True 13134
31.5%
2024-05-15T14:46:07.900126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Person Capacity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
2.0
18126 
4.0
12162 
3.0
5292 
6.0
3592 
5.0
2542 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters125142
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 18126
43.5%
4.0 12162
29.2%
3.0 5292
 
12.7%
6.0 3592
 
8.6%
5.0 2542
 
6.1%

Length

2024-05-15T14:46:07.985143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:08.089167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 18126
43.5%
4.0 12162
29.2%
3.0 5292
 
12.7%
6.0 3592
 
8.6%
5.0 2542
 
6.1%

Most occurring characters

ValueCountFrequency (%)
. 41714
33.3%
0 41714
33.3%
2 18126
14.5%
4 12162
 
9.7%
3 5292
 
4.2%
6 3592
 
2.9%
5 2542
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 41714
33.3%
0 41714
33.3%
2 18126
14.5%
4 12162
 
9.7%
3 5292
 
4.2%
6 3592
 
2.9%
5 2542
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 41714
33.3%
0 41714
33.3%
2 18126
14.5%
4 12162
 
9.7%
3 5292
 
4.2%
6 3592
 
2.9%
5 2542
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 41714
33.3%
0 41714
33.3%
2 18126
14.5%
4 12162
 
9.7%
3 5292
 
4.2%
6 3592
 
2.9%
5 2542
 
2.0%

Superhost
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.9 KiB
False
30055 
True
11659 
ValueCountFrequency (%)
False 30055
72.1%
True 11659
 
27.9%
2024-05-15T14:46:08.195190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Multiple Rooms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
0
29397 
1
12317 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41714
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Length

2024-05-15T14:46:08.419241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:08.513262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Most occurring characters

ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29397
70.5%
1 12317
29.5%

Business
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
0
27482 
1
14232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41714
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Length

2024-05-15T14:46:08.594281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-15T14:46:08.688302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Most occurring characters

ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27482
65.9%
1 14232
34.1%

Cleanliness Rating
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4422736
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:08.764329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88917304
Coefficient of variation (CV)0.094169379
Kurtosis14.678429
Mean9.4422736
Median Absolute Deviation (MAD)0
Skewness-2.8983188
Sum393875
Variance0.7906287
MonotonicityNot monotonic
2024-05-15T14:46:08.847348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 25042
60.0%
9 12444
29.8%
8 3031
 
7.3%
7 619
 
1.5%
6 347
 
0.8%
2 88
 
0.2%
4 83
 
0.2%
5 54
 
0.1%
3 6
 
< 0.1%
ValueCountFrequency (%)
2 88
 
0.2%
3 6
 
< 0.1%
4 83
 
0.2%
5 54
 
0.1%
6 347
 
0.8%
7 619
 
1.5%
8 3031
 
7.3%
9 12444
29.8%
10 25042
60.0%
ValueCountFrequency (%)
10 25042
60.0%
9 12444
29.8%
8 3031
 
7.3%
7 619
 
1.5%
6 347
 
0.8%
5 54
 
0.1%
4 83
 
0.2%
3 6
 
< 0.1%
2 88
 
0.2%

Guest Satisfaction
Real number (ℝ)

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.103179
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:08.959374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile80
Q190
median95
Q398
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1417451
Coefficient of variation (CV)0.087448627
Kurtosis19.018119
Mean93.103179
Median Absolute Deviation (MAD)4
Skewness-3.2577357
Sum3883706
Variance66.288014
MonotonicityNot monotonic
2024-05-15T14:46:09.074400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 8300
19.9%
98 3305
 
7.9%
97 3150
 
7.6%
96 3058
 
7.3%
95 2809
 
6.7%
93 2785
 
6.7%
94 2296
 
5.5%
90 2093
 
5.0%
99 1976
 
4.7%
92 1730
 
4.1%
Other values (41) 10212
24.5%
ValueCountFrequency (%)
20 91
0.2%
30 2
 
< 0.1%
40 67
0.2%
44 2
 
< 0.1%
47 11
 
< 0.1%
50 29
 
0.1%
53 10
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
56 5
 
< 0.1%
ValueCountFrequency (%)
100 8300
19.9%
99 1976
 
4.7%
98 3305
 
7.9%
97 3150
 
7.6%
96 3058
 
7.3%
95 2809
 
6.7%
94 2296
 
5.5%
93 2785
 
6.7%
92 1730
 
4.1%
91 1510
 
3.6%

Bedrooms
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1659395
Minimum0
Maximum10
Zeros3745
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:09.175422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63815668
Coefficient of variation (CV)0.54733259
Kurtosis8.7901581
Mean1.1659395
Median Absolute Deviation (MAD)0
Skewness1.3369614
Sum48636
Variance0.40724395
MonotonicityNot monotonic
2024-05-15T14:46:09.257441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 28800
69.0%
2 7830
 
18.8%
0 3745
 
9.0%
3 1253
 
3.0%
4 65
 
0.2%
9 10
 
< 0.1%
5 7
 
< 0.1%
6 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 3745
 
9.0%
1 28800
69.0%
2 7830
 
18.8%
3 1253
 
3.0%
4 65
 
0.2%
5 7
 
< 0.1%
6 2
 
< 0.1%
9 10
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 10
 
< 0.1%
6 2
 
< 0.1%
5 7
 
< 0.1%
4 65
 
0.2%
3 1253
 
3.0%
2 7830
 
18.8%
1 28800
69.0%
0 3745
 
9.0%

City Center (km)
Real number (ℝ)

UNIQUE 

Distinct41714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6797922
Minimum0.015044521
Maximum25.284557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:09.358464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.015044521
5-th percentile0.47776167
Q11.2759128
median2.2532367
Q33.5844892
95-th percentile6.2226982
Maximum25.284557
Range25.269512
Interquartile range (IQR)2.3085764

Descriptive statistics

Standard deviation1.9966841
Coefficient of variation (CV)0.74508914
Kurtosis9.6073479
Mean2.6797922
Median Absolute Deviation (MAD)1.0933857
Skewness2.188333
Sum111784.85
Variance3.9867474
MonotonicityNot monotonic
2024-05-15T14:46:09.473490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.022963798 1
 
< 0.1%
4.18319161 1
 
< 0.1%
2.884510486 1
 
< 0.1%
1.590720063 1
 
< 0.1%
1.630556014 1
 
< 0.1%
3.752866456 1
 
< 0.1%
2.004973756 1
 
< 0.1%
2.417770615 1
 
< 0.1%
4.244052067 1
 
< 0.1%
1.971111847 1
 
< 0.1%
Other values (41704) 41704
> 99.9%
ValueCountFrequency (%)
0.0150445207 1
< 0.1%
0.01505879807 1
< 0.1%
0.03466063682 1
< 0.1%
0.03981359742 1
< 0.1%
0.04278870764 1
< 0.1%
0.04331472743 1
< 0.1%
0.04333710768 1
< 0.1%
0.05129393071 1
< 0.1%
0.0522373372 1
< 0.1%
0.05223932064 1
< 0.1%
ValueCountFrequency (%)
25.28455675 1
< 0.1%
22.61745814 1
< 0.1%
22.61745145 1
< 0.1%
22.59511526 1
< 0.1%
21.29517392 1
< 0.1%
21.29515096 1
< 0.1%
20.89510207 1
< 0.1%
20.89509463 1
< 0.1%
20.49567803 1
< 0.1%
20.49565772 1
< 0.1%

Metro Distance (km)
Real number (ℝ)

UNIQUE 

Distinct41714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60392076
Minimum0.002301068
Maximum14.273577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:09.585515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.002301068
5-th percentile0.10505388
Q10.23669304
median0.39121979
Q30.67870246
95-th percentile1.764335
Maximum14.273577
Range14.271276
Interquartile range (IQR)0.44200942

Descriptive statistics

Standard deviation0.70620632
Coefficient of variation (CV)1.1693692
Kurtosis35.915312
Mean0.60392076
Median Absolute Deviation (MAD)0.1887107
Skewness4.5461255
Sum25191.95
Variance0.49872737
MonotonicityNot monotonic
2024-05-15T14:46:09.695540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.539380003 1
 
< 0.1%
0.1607415896 1
 
< 0.1%
0.2521760423 1
 
< 0.1%
0.1280245183 1
 
< 0.1%
0.1969495426 1
 
< 0.1%
0.3112300701 1
 
< 0.1%
0.1749464374 1
 
< 0.1%
0.07745505782 1
 
< 0.1%
0.1397255043 1
 
< 0.1%
0.2517577522 1
 
< 0.1%
Other values (41704) 41704
> 99.9%
ValueCountFrequency (%)
0.002301068012 1
< 0.1%
0.003220007615 1
< 0.1%
0.003935058041 1
< 0.1%
0.00394375911 1
< 0.1%
0.004750441048 1
< 0.1%
0.004762350151 1
< 0.1%
0.006157628218 1
< 0.1%
0.006170744841 1
< 0.1%
0.006388847148 1
< 0.1%
0.006405009016 1
< 0.1%
ValueCountFrequency (%)
14.27357693 1
< 0.1%
13.31411503 1
< 0.1%
13.31410827 1
< 0.1%
13.0699635 1
< 0.1%
11.68773401 1
< 0.1%
9.598773284 1
< 0.1%
9.573733182 1
< 0.1%
8.979488226 1
< 0.1%
8.918049525 1
< 0.1%
8.918036013 1
< 0.1%

Attraction Index
Real number (ℝ)

UNIQUE 

Distinct41714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.90599
Minimum15.152201
Maximum4513.5635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:09.817567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.152201
5-th percentile61.557055
Q1124.37161
median228.9206
Q3394.0002
95-th percentile748.05056
Maximum4513.5635
Range4498.4113
Interquartile range (IQR)269.62859

Descriptive statistics

Standard deviation235.75006
Coefficient of variation (CV)0.80212743
Kurtosis23.081567
Mean293.90599
Median Absolute Deviation (MAD)118.82209
Skewness2.7838479
Sum12259994
Variance55578.089
MonotonicityNot monotonic
2024-05-15T14:46:09.925591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.69037927 1
 
< 0.1%
201.2190128 1
 
< 0.1%
214.3731716 1
 
< 0.1%
318.9599196 1
 
< 0.1%
313.3012202 1
 
< 0.1%
186.550679 1
 
< 0.1%
281.7716448 1
 
< 0.1%
278.5517818 1
 
< 0.1%
165.177354 1
 
< 0.1%
277.8351091 1
 
< 0.1%
Other values (41704) 41704
> 99.9%
ValueCountFrequency (%)
15.15220147 1
< 0.1%
15.53291806 1
< 0.1%
16.58197413 1
< 0.1%
16.60073055 1
< 0.1%
16.60073515 1
< 0.1%
19.01913316 1
< 0.1%
19.01914767 1
< 0.1%
19.1378197 1
< 0.1%
19.13782442 1
< 0.1%
19.21394433 1
< 0.1%
ValueCountFrequency (%)
4513.563486 1
< 0.1%
4512.59517 1
< 0.1%
4512.345962 1
< 0.1%
4510.73735 1
< 0.1%
4510.436033 1
< 0.1%
4509.914049 1
< 0.1%
4022.618126 1
< 0.1%
3031.840298 1
< 0.1%
3028.991664 1
< 0.1%
2934.133441 1
< 0.1%
Distinct41697
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.719658
Minimum0.92630092
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:10.036617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.92630092
5-th percentile2.7993946
Q15.5107349
median9.9510862
Q315.467009
95-th percentile26.894929
Maximum100
Range99.073699
Interquartile range (IQR)9.9562744

Descriptive statistics

Standard deviation8.3791612
Coefficient of variation (CV)0.71496636
Kurtosis11.80769
Mean11.719658
Median Absolute Deviation (MAD)4.8136122
Skewness2.2337958
Sum488873.81
Variance70.210343
MonotonicityNot monotonic
2024-05-15T14:46:10.150642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 18
 
< 0.1%
4.166707868 1
 
< 0.1%
15.50944925 1
 
< 0.1%
9.071040312 1
 
< 0.1%
13.70116669 1
 
< 0.1%
13.54460062 1
 
< 0.1%
8.031760835 1
 
< 0.1%
13.50975236 1
 
< 0.1%
11.395778 1
 
< 0.1%
10.98821029 1
 
< 0.1%
Other values (41687) 41687
99.9%
ValueCountFrequency (%)
0.9263009179 1
< 0.1%
1.040227906 1
< 0.1%
1.040956301 1
< 0.1%
1.134200311 1
< 0.1%
1.135137404 1
< 0.1%
1.141278148 1
< 0.1%
1.142220498 1
< 0.1%
1.145817817 1
< 0.1%
1.147309596 1
< 0.1%
1.148114127 1
< 0.1%
ValueCountFrequency (%)
100 18
< 0.1%
99.9944775 1
 
< 0.1%
99.95215309 1
 
< 0.1%
99.93738572 1
 
< 0.1%
99.91914511 1
 
< 0.1%
99.27831239 1
 
< 0.1%
98.86827875 1
 
< 0.1%
98.54126635 1
 
< 0.1%
89.1228879 1
 
< 0.1%
83.4027044 1
 
< 0.1%

Restraunt Index
Real number (ℝ)

UNIQUE 

Distinct41714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.69262
Minimum19.576924
Maximum6696.1568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:10.262667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19.576924
5-th percentile86.569952
Q1210.45957
median519.58351
Q3860.70816
95-th percentile1659.0356
Maximum6696.1568
Range6676.5798
Interquartile range (IQR)650.24858

Descriptive statistics

Standard deviation520.64472
Coefficient of variation (CV)0.83078164
Kurtosis3.6622326
Mean626.69262
Median Absolute Deviation (MAD)318.65136
Skewness1.5411706
Sum26141856
Variance271070.92
MonotonicityNot monotonic
2024-05-15T14:46:10.373693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.25389587 1
 
< 0.1%
468.9668977 1
 
< 0.1%
587.152537 1
 
< 0.1%
863.8062671 1
 
< 0.1%
840.6237449 1
 
< 0.1%
446.4117672 1
 
< 0.1%
747.0280298 1
 
< 0.1%
639.2836314 1
 
< 0.1%
399.2993753 1
 
< 0.1%
821.5965399 1
 
< 0.1%
Other values (41704) 41704
> 99.9%
ValueCountFrequency (%)
19.5769238 1
< 0.1%
21.45579724 1
< 0.1%
21.45580338 1
< 0.1%
21.49691927 1
< 0.1%
25.02270288 1
< 0.1%
26.50937148 1
< 0.1%
26.72904038 1
< 0.1%
27.90161264 1
< 0.1%
27.90167346 1
< 0.1%
27.93416764 1
< 0.1%
ValueCountFrequency (%)
6696.156772 1
< 0.1%
4592.883342 1
< 0.1%
4591.339847 1
< 0.1%
4590.349641 1
< 0.1%
4590.306687 1
< 0.1%
4589.772131 1
< 0.1%
4589.32312 1
< 0.1%
4552.357526 1
< 0.1%
4542.75415 1
< 0.1%
4515.190626 1
< 0.1%
Distinct41697
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.55359
Minimum0.59275692
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 KiB
2024-05-15T14:46:10.493720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.59275692
5-th percentile2.4548125
Q111.132052
median21.814414
Q336.821356
95-th percentile61.666917
Maximum100
Range99.407243
Interquartile range (IQR)25.689304

Descriptive statistics

Standard deviation18.484572
Coefficient of variation (CV)0.72336499
Kurtosis0.28328231
Mean25.55359
Median Absolute Deviation (MAD)12.606905
Skewness0.8473132
Sum1065942.4
Variance341.67941
MonotonicityNot monotonic
2024-05-15T14:46:10.605745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 18
 
< 0.1%
30.41421289 1
 
< 0.1%
20.59401572 1
 
< 0.1%
34.46214487 1
 
< 0.1%
29.49164454 1
 
< 0.1%
18.42061123 1
 
< 0.1%
37.90216411 1
 
< 0.1%
27.30897089 1
 
< 0.1%
27.79045368 1
 
< 0.1%
28.51790328 1
 
< 0.1%
Other values (41687) 41687
99.9%
ValueCountFrequency (%)
0.5927569191 1
< 0.1%
0.6407212807 1
< 0.1%
0.6460305932 1
< 0.1%
0.6549731588 1
< 0.1%
0.6608714797 1
< 0.1%
0.6659262496 1
< 0.1%
0.6670097117 1
< 0.1%
0.6677877225 1
< 0.1%
0.6743712124 1
< 0.1%
0.6746584383 1
< 0.1%
ValueCountFrequency (%)
100 18
< 0.1%
99.96639378 1
 
< 0.1%
99.94483419 1
 
< 0.1%
99.94389898 1
 
< 0.1%
99.92248394 1
 
< 0.1%
99.19076175 1
 
< 0.1%
98.48673907 1
 
< 0.1%
98.30841086 1
 
< 0.1%
98.11497172 1
 
< 0.1%
98.05204 1
 
< 0.1%

Interactions

2024-05-15T14:46:05.364892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.224991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.289072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.286295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.372541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.312753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.356988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.346211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.289424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.252641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.463915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.338857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.391094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.389319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.469563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.409775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.459011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.441233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.389446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.470690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.564938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.443880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.496118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.495343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.570586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.509798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.564036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.541255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.493470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.577715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.667961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.547904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.602142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.601367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.671609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.611820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.670059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.642278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.596493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.685739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.758981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.641925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.697162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.775406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.760628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.700840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.766081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.735300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.689515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.780760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.851002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.736946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.793185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.871428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.850648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.791861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.862103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.827320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.781535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.876782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.951024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.903984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.896208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.976452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.945670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.888883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.963125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.924342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.880558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.979806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:06.040045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:56.996005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.988229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.070473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.032691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.976902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.053145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.009361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.969578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.070826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:06.132066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.090027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.085250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.169495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.124711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.068923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.149167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.102382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.061599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.166847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:06.231087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:57.193049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:58.188273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:45:59.274519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:00.221734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:01.168947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:02.250190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:03.197403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:04.160621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-15T14:46:05.268871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-05-15T14:46:06.377120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-15T14:46:06.663185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CityPriceDayRoom TypeShared RoomPrivate RoomPerson CapacitySuperhostMultiple RoomsBusinessCleanliness RatingGuest SatisfactionBedroomsCity Center (km)Metro Distance (km)Attraction IndexNormalised Attraction IndexRestraunt IndexNormalised Restraunt Index
0Amsterdam194.033698WeekdayPrivate roomFalseTrue2.0False1010.093.015.0229642.53938078.6903794.16670898.2538966.846473
1Amsterdam344.245776WeekdayPrivate roomFalseTrue4.0False008.085.010.4883890.239404631.17637833.421209837.28075758.342928
2Amsterdam264.101422WeekdayPrivate roomFalseTrue2.0False019.087.015.7483123.65162175.2758773.98590895.3869556.646700
3Amsterdam433.529398WeekdayPrivate roomFalseTrue4.0False019.090.020.3848620.439876493.27253426.119108875.03309860.973565
4Amsterdam485.552926WeekdayPrivate roomFalseTrue2.0True0010.098.010.5447380.318693552.83032429.272733815.30574056.811677
5Amsterdam552.808567WeekdayPrivate roomFalseTrue3.0False008.0100.022.1314201.904668174.7889579.255191225.20166215.692376
6Amsterdam215.124317WeekdayPrivate roomFalseTrue2.0False0010.094.011.8810920.729747200.16765210.599010242.76552416.916251
7Amsterdam2771.307384WeekdayEntire home/aptFalseFalse4.0True0010.0100.031.6868071.458404208.80810911.056528272.31382318.975219
8Amsterdam1001.804420WeekdayEntire home/aptFalseFalse4.0False009.096.023.7191411.196112106.2264565.624761133.8762029.328686
9Amsterdam276.521454WeekdayPrivate roomFalseTrue2.0False1010.088.013.1423610.924404206.25286210.921226238.29125816.604478
CityPriceDayRoom TypeShared RoomPrivate RoomPerson CapacitySuperhostMultiple RoomsBusinessCleanliness RatingGuest SatisfactionBedroomsCity Center (km)Metro Distance (km)Attraction IndexNormalised Attraction IndexRestraunt IndexNormalised Restraunt Index
41704Vienna463.501858WeekendEntire home/aptFalseFalse5.0False1010.090.021.0218780.285141176.75490012.658020283.4264596.850308
41705Vienna727.391721WeekendEntire home/aptFalseFalse6.0False0110.096.030.5685620.230806209.89871915.031561411.5536339.947093
41706Vienna718.275951WeekendEntire home/aptFalseFalse6.0False0110.095.030.5658540.136006212.07761915.187600420.03013810.151966
41707Vienna115.933899WeekendPrivate roomFalseTrue4.0False109.094.013.0419320.308192109.7513877.859670208.5178875.039797
41708Vienna750.765491WeekendEntire home/aptFalseFalse6.0False0110.096.030.3788040.203138257.49481718.440080548.97329613.268473
41709Vienna715.938574WeekendEntire home/aptFalseFalse6.0False0110.0100.030.5301810.135447219.40247815.712158438.75687410.604584
41710Vienna304.793960WeekendEntire home/aptFalseFalse2.0False008.086.010.8102050.100839204.97012114.678608342.1828138.270427
41711Vienna637.168969WeekendEntire home/aptFalseFalse2.0False0010.093.010.9940510.202539169.07340212.107921282.2964246.822996
41712Vienna301.054157WeekendPrivate roomFalseTrue2.0False0010.087.013.0441000.287435109.2365747.822803158.5633983.832416
41713Vienna133.230489WeekendPrivate roomFalseTrue4.0True1010.093.011.2639320.480903150.45038110.774264225.2472935.444140